Method and apparatus of generating update parameters and displaying correlated keywords

ABSTRACT

Provided is a method of generating updating parameters. The method obtains search keywords used by users within a predetermined time period; counts the search keywords to obtain primary keywords, related keywords, co-search frequencies of each primary keyword and the respective related keywords being searched together, and search frequencies of the primary keywords being searched alone; computes first feature values based on the search frequencies of the primary keywords being searched alone; and then computes second feature values based on the first feature values and the co-search frequencies of the primary keywords and the respective related keywords. The second feature values serve as updating parameters for determining displaying modes of the related keywords. An apparatus of generating updating parameters, and a method and an apparatus of displaying related keywords according to the updating parameters are also provided. The solution keeps abreast with the user trends to allow a better user experience and improve computing performance and efficiency. For a service provider, no special secret algorithm is needed, and the operation is easy with a low development cost.

RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 12/594,930, filed on Nov. 6, 2009 which was thenational stage application based on international patent applicationPCT/CN2007/070573 filed on Aug. 28, 2007, which claims priority fromChinese Patent Application No. 200710095848.7, which was filed in ChinaPatent Office on Apr. 10, 2007, entitled “METHOD AND APPARATUS OFGENERATING UPDATE PARAMETERS AND DISPLAYING CORRELATED KEYWORDS,” whichapplications are hereby incorporated in their entirety by reference.

TECHNICAL FIELD

The present invention relates to the field of data processing, andparticularly relates to methods and apparatuses of generating updatingparameters, and methods and apparatuses of displaying related keywords.

BACKGROUND ART

Along with the rapid increase of the use of texts and multimediacontents on the Internet and other data networks and systems, a user hasbecome increasingly reliant on keyword-based search tools to findrequired information. Normally, a user enters a keyword of an inquiredinformation document into a search tool or engine. The search tool orengine then performs a search in an indexed database and returns asearch result. Generally, existing search tools or engines may furtherdisplay, on a current web page or a current result page, one or morerelated keywords corresponding to the user input keyword (i.e., theprimary keyword).

As commonly known, most users begin an online information search at asearch engine, and generally search the needed information by inputtinga keyword. With the accelerating pace of social changes and ongoingcultural developments, many of the fixed keywords are failing to meetthe various needs of users. In particular, existing keyword searchmethods are no longer able to satisfy various needs of the users due tothe information explosion. Keyword records are fixed, may have beencreated at a much earlier time, and have infrequent updates, making themunable to satisfy the requirements of the changing online contents. Take“clothes” as an example of a primary keyword. Usually, related keywordsobtained for this primary keyword using existing technologies are wordslike “activewear” and “down coat”, etc. As the season changes, however,the related keywords that are actually wanted by a user may be “springfashion”, “summer fashion”, and “T-shirt”, etc. Keywords obtained usingexisting technologies do not adapt to a usage trend of the user.

As can be seen, keyword search of existing technologies cannot satisfythe needs of users, especially in that the keywords used do not adapt tothe usage trends of the users.

DESCRIPTION OF THE INVENTION

The present invention is to provide a method and an apparatus ofgenerating updating parameters such that keywords used can meet a usagetrend of a user.

Correspondingly, another technical goal is to provide a method and anapparatus of displaying related keywords in order to ensure that a usercan obtain related keywords in a simple and comprehensive manner.

In order to achieve the above goals, exemplary embodiments of thepresent invention disclose a method of generating updating parameters.The method obtains search keywords used by users within a predeterminedtime period; counts the search keywords to obtain primary keywords,related keywords, co-search frequencies of each primary keyword and therespective related keywords being searched together, and searchfrequencies of the primary keywords being searched alone; computes firstfeature values based on the search frequencies of the primary keywordsbeing searched alone; and then computes second feature values based onthe first feature values and the co-search frequencies of the primarykeywords and the respective related keywords. The second feature valuesserve as updating parameters for determining displaying modes of therelated keywords.

Preferably, the method further records the primary keywords, the relatedkeywords and the second feature values to form a keyword informationtable.

Preferably, the above steps of counting the search keywords, computingthe first feature values and computing the second feature values areconcurrently executed using a multi-threading method.

Preferably, prior to computing the first feature values, the methodfurther filters out search keywords that satisfy a filtering rule.

Preferably, to compute the second feature values, the method computescorrelation levels based on the co-search frequencies of each primarykeyword and the respective related keywords being searched together; andobtains the first feature values from a cache, and computing the secondfeature values based on the first feature values and the correlationlevels.

Preferably, the keyword information table contains the first featurevalues corresponding to the primary keywords.

Preferably, the search keywords include search keywords used by searchusers and promulgated keywords posted by promulgating users.

The exemplary embodiments of the present invention further disclose anapparatus of generating updating parameters. In the apparatus, anacquisition unit is used for obtaining search keywords used by userswithin a predetermined time period; a statistics unit is used forcounting the search keywords to obtain primary keywords, relatedkeywords, co-search frequencies of each primary keyword and therespective related keywords being searched together, and searchfrequencies of the primary keywords being searched alone; a firstcomputation unit is used for computing first feature values based on thesearch frequencies of the primary keywords being searched alone; and asecond computation unit is used for computing second feature valuesbased on the first feature values and the co-search frequencies of eachprimary keyword and the respective related keywords being searchedtogether. The second feature values serve as updating parameters fordetermining displaying modes of the related keywords.

Preferably, the apparatus further includes a recording unit used forrecording the primary keywords, the related keywords and the secondfeature values to form a keyword information table.

Preferably, the apparatus further includes a filtering unit connectedwith the statistics unit and used for filtering out search keywords thatsatisfy a filtering rule.

Preferably, the above the second computation unit may include acorrelation computing sub-unit used for computing correlation levelsbased on the co-search frequencies of each primary keyword and therespective related keywords being searched together; and anacquiring/computing sub-unit used for obtaining the first feature valuesfrom a cache and computing the second feature values based on the firstfeature values and the correlation levels.

Preferably, the apparatus further includes an addition unit which isconnected with the recording unit and used for recording the firstfeature values in the keyword information table.

The exemplary embodiments of the present invention further disclose amethod of displaying related keywords. The method submits a request foracquiring related keywords based on a primary keyword inputted by auser. Based on the request, the method obtains related keywords whosesecond feature values are greater than or equal to a threshold. TheSecond feature values are computed based on a first feature value andco-search frequencies of the primary keyword and related keywords beingsearched together. The first feature value is computed based on a searchfrequency of the primary keyword being searched alone. The co-searchfrequencies of the primary keyword and related keywords being searchedtogether and the search frequency of the primary keyword being searchedalone are obtained by counting search keywords. Method then displays theobtained related keywords.

Preferably, the method further displays the related keywords havingsecond feature values smaller than the threshold.

Preferably, the method obtains the related keywords whose second featurevalues are greater than or equal to the threshold from a keywordinformation table containing the primary keyword, the related keywordsand the second feature values.

The exemplary embodiments of the present invention further disclose anapparatus of displaying related keywords. The apparatus includes aninterface unit used for submitting a request for acquiring relatedkeywords corresponding to a primary keyword inputted by a user. Theapparatus also includes a related keyword acquisition unit used foracquiring related keywords whose second feature values are greater thanor equal to a threshold according to the request. The second featurevalues are obtained based on a first feature value and co-searchfrequencies of the primary keyword and the related keywords beingsearched together, where the first feature value is obtained based on asearch frequency of the primary keyword being searched alone. Thefrequencies of the primary keyword and related keywords being searchedtogether and the frequency of the primary keyword being searched aloneare obtained by counting search keywords. The apparatus further includesa first display unit used for displaying the acquired related keywords.

Preferably, the apparatus may further include a second display unitwhich is used for displaying related keywords having a second featurevalue smaller than the threshold.

As illustrated, the exemplary embodiments conduct statistical analyseson search keywords used by users within a predetermined time period inorder to ensure timely relevance of the keywords. Using the secondfeature values as parameters for determining displaying modes ofassociated related keywords, the related keywords that satisfy currentusage trends are provided to the users with preference, resulting in abetter user experience.

DESCRIPTION OF DRAWINGS

FIG. 1 shows a flow chart of an exemplary method of generating updatingparameters in accordance with the present invention.

FIG. 2 shows a structural diagram of an exemplary apparatus ofgenerating updating parameters in accordance with the present invention.

FIG. 3 shows a flow chart of an exemplary method of displaying relatedkeywords in accordance with the present invention.

FIG. 4 shows a structural diagram of an exemplary apparatus ofdisplaying related keywords in accordance with the present invention.

EXEMPLARY EMBODIMENTS

In order to more clearly and easily understand the goals,characteristics and advantages of the present invention, the followingexemplary embodiments illustrated the invention in further detail.

The exemplary embodiments of the present invention provide relatedkeywords that satisfy the user search demand better based on updatingcorrelation parameters between a primary keyword and correspondingrelated keywords, and control the outputs of the respective relatedkeywords according to the updating correlation parameters.

FIG. 1 shows a flow chart of an exemplary method of generating updatingparameters in accordance with the present invention. The method includesthe following procedures.

Block 101 obtains search keywords used by users within a predeterminedtime period.

The predetermined time period may be predetermined by one skilled in theart according to the needs. For example, on a shopping website, in orderto have related product keywords that satisfy usage trends of the users,the predetermined time period may be set up to be one week or one month,etc. The search keywords may come from a database, a script program, alocal program, a historical record of user inputs, a user client, aserver or a storage unit of other device, etc. The present inventiondoes not restrict the manner of doing this.

Block 102 counts the search keywords to obtain primary keywords, relatedkeywords, co-search frequencies of each primary keyword and therespective related keywords being searched together, and searchfrequencies of the primary keywords being searched alone.

Since keywords in existing techniques are fixed and were created at anearlier time with infrequent updates, these techniques are unsuited forreal-time updates and usages of active online contents, and thereforecannot satisfy the user demands. The solution provided herein is to usethe statistics of the primary keywords and the related keywords toensure that users obtain related keywords that satisfy the changingusage trend. In practice, any available method may be used to obtain theprimary keywords, the related keywords, the co-search frequencies ofeach primary keyword and the respective related keywords being searchedtogether, and the search frequencies of the primary keywords beingsearched alone. For example, a search keyword may be taken as a primarykeyword, and the keywords that are searched together with the primarykeyword are taken as the related keywords of the primary keyword. Theco-search frequencies of each primary keyword and the respective relatedkeywords being searched together, and the search frequencies of theprimary keywords being searched alone are then separately counted.

The following counting method that is based on Apriori algorithm is usedas an example to illustrate. A basic process of the Apriori algorithmincludes: (1) scan a transactional database to find all items having asupport not less than a minimum support level to form a frequent itemsetL1; (2) link items in L1; (3) scan the transactional database byfiltering the items in L1 to obtain an itemset L2 having a support notless than another minimum support level; (4) link items in L2; (5) scanthe transactional database by filtering the items in L2 to obtain anitemset L3 having a support not less than still another minimum supportlevel; and so forth. TABLE 1 shows search keywords obtained using thismethod. TABLE 2 shows primary keywords and their respective frequenciesof being searched alone. TABLE 3 shows a further count of the relatedkeywords that have been searched together with the primary keywords, andthe respective frequencies of being searched together.

TABLE 1 1 beer, gum, napkin 2 beer, peanut, gum 3 beer, milk 4 milk,sugar 5 peanut, candies

TABLE 2 Primary keyword Frequency beer 3 peanut 2 gum 2 napkin 1 milk 2sugar 1

TABLE 3 Primary keyword and Frequency of being related keyword searchedtogether beer, peanut 1 beer, gum 2 beer, napkin 1 beer, milk 1 beer,sugar 0 peanut, gum 1 peanut, napkin 0 peanut, milk 0 peanut, sugar 0gum, napkin 1 gum, milk 0 gum, sugar 0 napkin, milk 0 napkin, sugar 0milk, sugar 1

Primary keywords, related keywords, co-search frequencies of eachprimary keyword and the respective related keywords being searchedtogether, and search frequencies of the primary keywords being searchedalone are therefore obtained using the above rules of countingfrequencies.

The above method is only for an illustrative purpose. One may use othermethods such as mining association rules according to experiences orneeds. The present invention does not restrict the manner of doing this.

Preferably, the present exemplary embodiment may further include aprocedure of removing search terms that satisfy a filtering rule. Thefiltering rule may be predetermined by one skilled in the art based onexperience or needs. For example, TABLE 4 represents a remaining resultof applying a filtering rule which removes items having a frequency of aprimary keyword and a related keyword being searched together that isless than two.

TABLE 4 Primary Keyword and Frequency of Being Related keyword SearchedTogether beer, gum 2

Alternatively, the filtering rule may be set up to remove searchkeywords that are invalid keywords or search keywords that have invalidsymbols or invalid phrases. The present invention does not restrict themanner of doing this.

Block 103 computes first feature values based on the frequencies of theprimary keywords being searched alone.

For the purpose of meeting usage trends of keywords to better satisfysearch demands of the users, the first feature values may be interpretedto represent the popularity of the respective keyword. Under thiscircumstance, a first feature value may be obtained through a comparisonbetween the frequency of a primary keyword being searched alone and apredetermined popularity value. One formula of computing a first featurevalue may therefore be:First feature value=Frequency of a primary keyword being searchedalone/Predetermined popularity base value.

TABLE 5 shows an example.

TABLE 5 Frequencies of primary Frequency of keyword and related primaryPrimary keywords being keyword being keyword Related keyword searchedtogether searched alone bike E bike, city bike, 2, 1, 1 2 bicycle e bikebike, city bike, 1, 1, 1 2 bicycle city bike bike, e bike 1, 1 1

In the above table, if the predetermined popularity base value istwenty, the first feature value for the keyword “bike” will be 2/20=0.1,while the first feature value of the keyword “e bike” is 1/20=0.05.Preferably, the popularity base value is a median of the searchfrequencies of the primary keywords being searched alone. For example,if ten percent of the primary keywords have been each searched alone forten times, eighty percent of the primary keywords have been eachsearched alone for twenty times, and ten percent of the primary keywordshave been each searched alone for fifty times, the value twenty will beused as the popularity base value. One may determine this popularitybase value in advance based on experiences or needs. The presentinvention does not restrict the manner of doing this.

One may determine the first feature values and the method for computingthe first feature values based on experiences or needs. The above methodis used only for an illustrative purpose. The present invention does notimpose restrictions in this regard.

Block 104 computes second feature values based on the first featurevalues and the co-search frequencies of each primary keyword and therespective related keywords being searched together. The second featurevalues are used as updating parameters for determining displaying modesof the related keywords.

Based on the foregoing exemplary embodiments, it is understood thatcertain correlation parameters between primary keywords and relatedkeywords need to be acquired in practical applications. In thisexemplary embodiment, the second feature values obtained based on thefirst feature values and the co-search frequencies of each primarykeyword and the respective related keywords being searched together area representative example of such correlation parameters. In order toallow the second feature values to fully reflect the usage trends of thekeywords, the following procedures are preferably employed in thepresent exemplary embodiment to compute the second feature values.

A sub-block A1 computes correlation levels based on the frequencies ofeach primary keyword and the respective related keywords being searchedtogether.

A sub-block A2 obtains the first feature values from a cache, andcomputes the second feature values based on the first feature values andthe correlation levels.

Specifically, the correlation levels are first computed based on thefrequencies of each primary keyword and the respective related keywordsbeing searched together. One formula of computing a correlation levelmay be:Correlation level=Frequency of a primary keyword and a related keywordbeing searched together/Predetermined base value of correlation level.

Using the data in TABLE 5, if the predetermined base value ofcorrelation level is ten, the correlation level between the primarykeyword “bike” and the related keyword “e bike” corresponds to thefrequency of co-appearance of “bike” and “e bike” in the table dividedby ten, that is, 2/10=0.2. Preferably, the base value of correlationlevel may be a median of the frequencies of each primary keyword and therespective related keywords being searched together. One maypredetermine this value based on experiences or needs. The presentinvention does not restrict the manner of doing this.

In order to improve the computing efficiency, the present exemplaryembodiment may store the first feature values in a cache and retrievethem directly from the cache when the second feature values arecomputed. The second feature values may be computed based on the firstfeature values and the correlation levels. It is appreciated thatretrieving data from a cache is much faster than retrieving data from adatabase or other devices. Therefore, the preferred embodiment of thepresent invention may have a better computing performance. Furthermore,storing data in a cache may include saving in the form of a hash, savingin the form of a file, or saving in other forms. In order to facilitatethe retrieving of the first feature values, operations such as sortingor ranking the primary keywords may be set up for optimization. Thepresent invention does not have any limitation on the method ofoptimization.

In order to obtain the second feature values that more closely satisfyuser needs, the first feature values and the correlation levels arepreferably weighted separately, and the weighted results are taken asthe second feature values. For example, if the first feature value ofthe keyword “e bike” in the previous example is 0.05 with a weight 0.4,and the correlation level between the primary keyword “bike” and therelated keyword “e bike” is 0.2 with a weight 0.6, then the secondfeature value for the primary keyword “bike” and the related keyword “ebike” is obtained as 0.05×0.4+0.2×0.6=0.14.

The weights may be set up in advance based on experiences or needs andmay also be freely modified based on user needs. The present inventiondoes not restrict the manner of doing this. In order to ensureconsistency of computation results, the sum of the weights may be set tobe one, or another value.

It is possible for one skilled in the art to use other schemes forcomputing second feature values. The present invention does not restrictthe manner of doing this.

The second feature values are taken as updating parameters thatdetermine displaying modes of corresponding related keywords. Forexample, related keywords that have a second feature value greater thanor equal to a threshold are preferably or constantly displayed, whilerelated keywords that have a second feature value smaller than thethreshold are either displayed in a rotating manner or not displayed.The method of displaying the related keywords based on second featurevalues can be freely set up by one skilled in the art based onexperiences or needs. The present invention does not restrict the mannerof doing this.

Preferably, the step of counting search keywords, and the steps ofcomputing the first feature values and computing the second featurevalues are concurrently executed using a multi-threading method in orderto improve computing performance and computing efficiency of the system.

The multi-threading mechanism allows concurrent execution of multipleinstruction streams with each instruction stream being a thread. Eachthread is mutually independent from one another. Execution of multiplethreads is concurrent, that is, simultaneous in logical sense.Specifically, multi-threading operation refers to a situation where Nexecutives exist at the same time and are executed simultaneouslyaccording to several different execution threads. In order to improvecomputing performance and computing efficiency of the exemplaryembodiments, a thread of counting search keywords and threads ofcomputing the feature values (including the first feature values and thesecond feature values) are executed concurrently to process thecorresponding related keywords in a rotating manner.

Preferably, the exemplary embodiments may further record the primarykeywords, the related keywords and the second feature values. Therecording may be done in a form, in a file, or any other suitable ways.More preferably, a keyword information table is formed by recording theprimary keywords, the related keywords and the second feature values.During the next update, it may only be necessary to remove the existingdata in the keyword information table and fill the table again with theupdating data according to the exemplary method in the presentinvention. The update may be performed regularly, in real time oralternated between the two. For example, update may be performed oncefor each month. Alternatively, the update may be performed freely by atechnical person in the art. The present invention does not restrict themanner of doing this.

In order to provide a more intuitive display that directly allows a userto obtain a search hint during a search, the exemplary method preferablyfurther records related first feature values in the keyword informationtable to further improve the intelligence of the search tool.

In practical applications, one possible scenario is to provide differentkeywords to different types of users. For example, on a shoppingwebsite, users generally include those who buy and those who sell. Underthis circumstance, search keywords may include both search keywords usedby search users and promulgated keywords posted by promulgating users.

In order to allow one to understand the present invention more clearly,the present invention is described in details using an example in whichsearch keywords include search keywords used by search users (referredto as first search keywords) and promulgated keywords posted bypromulgating users (referred to as second search keywords).Specifically, the example includes the following procedure.

A procedure A obtains first search keywords in a first script programwithin a predetermined time period. The first search keywords come fromsearch keywords used in searches from the time when a user opens abrowser to the time when the user closes the browser. For example, inone instance of using a browser, a user conducted multiple searchesusing a search field and entered multiple keywords. These keywords arecounted as the first search keywords in this example. TABLE 6 shows thefirst search keywords, the first primary keywords, the first relatedkeywords, the frequencies of the first primary keywords and the firstrelated keywords being searched together, and the frequencies of thefirst primary keywords being searched alone, which are obtainedstatistically.

TABLE 6 Frequencies of the first Frequency of the First primary keywordand first first primary primary First related related keywords beingkeyword being keyword keywords searched together searched alone bike ebike, city 2, 1, 1 2 bike, bicycle e bike bike, city 1, 1, 1 2 bike,bicycle city bike bike, e bike 1, 1 1

A procedure B obtains second search keywords in a second script programwithin the predetermined time period. The second search keywords comefrom keywords inputted by users posting about products. These keywordsmay be obtained as usually more than three keywords are posted. TABLE 7shows the second search keywords, the second primary keywords, thesecond related keywords, the frequencies of the second primary keywordsand the second related keywords being searched together, and thefrequencies of the second primary keywords being searched alone, whichare obtained statistically.

TABLE 7 Frequencies of second Frequency of Second keyword and secondsecond keyword primary Second related related keywords being searchedkeyword keywords being searched together alone bike e bike, city 1, 2, 12 bike, bicycle e bike bike, city bike 1, 1 2 city bike bike, e bike, 2,1, 1 1 bicycle

A procedure C computes first feature values as follows. If thepredetermined base value of popularity is twenty, the first featurevalue represented by the popularity level of “bike” in the first primarykeywords would be 2/20=0.1, and first feature value represented by thepopularity level of “e bike” in the first related keywords would be1/20=0.05. The first feature values for the second primary keywords andthe second related keywords are computed in the same manner above.

A procedure D computes correlation levels as follows. If thepredetermined base value of correlation level is ten, a firstcorrelation level and a second correlation level between the primarykeyword “bike” and the related keyword “e bike” are then 2/10=0.2 and1/10=0.1, respectively. Based on experience, weights are separatelyassigned. The weight for the first feature value is 0.2, the weight forthe first correlation level is 0.3 and the weight for the secondcorrelation level is 0.5. Therefore, the second feature value for theprimary keyword “bike” and the related keyword “e bike” is0.2×0.05+0.3×0.2+0.5×0.1=0.12.

As the first feature values for the first keyword and the second keywordare the same in the above example, only one first feature value is usedin the computation in order to improve computing efficiency. It isappreciated that the computation result can be obtained by applying thetwo first feature values in the computation and separately assigningdifferent weights thereto.

A procedure E records the primary keywords, the related keywords and thesecond feature values to form a keyword information table as shown inTABLE 8.

TABLE 8 Primary keyword Related keywords Values for related keywordsbike e bike, city bike 0.12, . . . . . . . . . . . .

FIG. 2 shows a schematic structural diagram of an exemplary apparatus ofgenerating updating parameters in accordance with the present invention.In the apparatus, an acquisition unit 201 is used for obtaining searchkeywords used by users within a predetermined time period; a statisticsunit 202 is used for counting the search keywords to obtain primarykeywords, related keywords, co-search frequencies of each primarykeyword and the respective related keywords being searched together, andsearch frequencies of the primary keywords being searched alone; a firstcomputation unit 203 is used for computing first feature values based onthe search frequencies of the primary keywords being searched alone; anda second computation unit 204 is used for computing second featurevalues based on the first feature values and the co-search frequenciesof each primary keyword and the respective related keywords beingsearched together. The second feature values serve as updatingparameters that determine displaying modes of the related keywords.

Preferably, the apparatus may further include a recording unit, which isused for recording the primary keywords, the related keywords and thesecond feature values to form a keyword information table.

Preferably, the statistics unit, the first computation unit and thesecond computation unit are used for processing concurrentmulti-threading operations.

Preferably, the apparatus may further include a filtering unit, which isused for filtering search keywords that meet a filtering rule.

Preferably, the second computation unit includes a correlation computingsub-unit for computing correlation levels based on the co-searchfrequencies of each primary keyword and the respective related keywordsbeing searched together; and an acquiring/computing sub-unit forobtaining the first feature values in a cache and computing the secondfeature values based on the first feature values and the correlationlevels.

Preferably, the apparatus may further include an addition unit, which isused for recording relevant first feature values in the keywordinformation table.

Preferably, the search keywords include search keywords used by searchusers and promulgated keywords posted by promulgating users.

As the exemplary apparatus of generating updating parameters shown inFIG. 2 can correspond to the foregoing exemplary method of generatingupdating parameters, description of the exemplary apparatus isrelatively brief. Any missing details may refer to descriptions ofrelated foregoing portions in the present invention.

FIG. 3 shows a method of adding related keywords in accordance with thepresent invention. The method includes the following procedures.

Block 301 submits a request for acquiring related keywords correspondingto a primary keyword inputted by a user.

Block 302 obtains, according to the request, related keywords that havea second feature value greater than or equal to a threshold. The secondfeature values are computed based on a first feature value and co-searchfrequencies of the primary keyword and related keywords being searchedtogether. The first feature value is computed based on a searchfrequency of the primary keyword being searched alone. The co-searchfrequencies of the primary keyword and related keywords being searchedtogether, and the search frequency of the primary keyword being searchedalone are obtained by counting search keywords.

Block 303 displays the obtained related keywords.

When using a search tool or a search engine, a user may use an inputdevice such as a keyboard or a writing pad to input a primary keywordinto a search box or a toolbar, followed by clicking for confirmation,pressing a “Enter” button, pressing a “Tab” button, or any othertriggering method, to trigger a local program or a script program of asearch page to submit a request for acquiring the related keywordscorresponding to the primary keyword.

The second feature values represent a kind of correlation parameters. Inorder to allow the second feature values to fully reflect the usagetrends of the keywords, the present exemplary embodiment preferablyfirst computes correlation levels based on the frequencies of theprimary keyword and related keywords being searched together. Forexample, one formula of computing a correlation level may be:Correlation level=Frequency of a primary keyword and a related keywordbeing searched together/Predetermined base value of correlation level.

Preferably, the base value of correlation level is a median of thefrequencies of the primary keyword and related keywords being searchedtogether, and can be predetermined by one skilled in the art based onexperience or needs. The present invention does not restrict the mannerof doing this.

Results that are computed based on the first feature values and thecorrelation levels can be used as the second feature values.

As another exemplary embodiment, the first feature values and thecorrelation levels can be separately weighted, and results obtained canbe used as the second feature values. The weights can be predeterminedbased on experience or needs. Alternatively, the weights can be freelymodified as needed. The present invention does not restrict the mannerof doing this. In order to ensure the consistency of computationresults, the sum of the weights can be set to be one.

It is appreciated that in one skilled the art may use other methods forcomputing second feature values. The present invention does not restrictthe manner of doing this.

In practice, the related keywords whose have a second feature valuegreater than or equal to the threshold may be displayed every time whenthe related keywords corresponding to the primary keyword are displayed.Having a second feature value greater than or equal to the thresholdindicates that these related keywords are more correlated to the primarykeyword. Given this, each time when the related keywords are displayed,a user is provided with highly recommended items that are closelyaligned with a usage habit of the user and satisfy a usage trend of theuser. This results in a better user experience. For example, relatedkeywords having a second feature value greater than or equal to athreshold (e.g., 0.2) corresponding to the primary keyword “bike” are:electric bike, mountain bike, e bike, e bicycle, suspension bike,scooter, motorcycle, electric scooter, gas scooter and vehicle. In caseof a search tool using fixed related keywords, these ten relatedkeywords may constantly appear on the related web page every time whenthe primary keyword “bike” is searched. In case of a search tooldisplaying relative keywords in a rotating manner, these ten relatedkeywords may be presented in every rotated related keyword group. When auser submits a request for acquiring related keywords corresponding to“bike”, these ten related keywords will appear in the related keywordgroup that is displayed to the user, regardless of which related keywordgroup is presented according to a frequency of the request.

Preferably, related keywords having a second feature value smaller thanthe threshold may be displayed according to an arbitrary rule, or maynot be displayed at all. For example, for search tools that only displaya fixed number of related keywords, only a fixed number of relatedkeywords having a second feature value greater than or equal to athreshold may be displayed, while the related keywords having a secondfeature value smaller than the threshold are not displayed. For searchtools that display related keywords in a rotating manner or in afull-list display, these related keywords may be displayed according toan arbitrary rule. The present invention does not restrict the manner ofdoing this.

FIG. 4 shows a schematic structural diagram of an exemplary apparatus ofadding related keywords in accordance with the present invention. In theapparatus, an interface unit 401 is used for submitting a request foracquiring related keywords corresponding to a primary keyword inputtedby a user. A related keyword acquisition unit 402 is used for acquiringrelated keywords having a second feature value greater than or equal toa threshold according to the request. The second feature values areobtained based on first feature values and co-search frequencies of theprimary keyword and related keywords being searched together. The firstfeature value is obtained based on a search frequency of the primarykeyword being searched alone. The co-search frequencies of the primarykeyword and related keywords being searched together and the searchfrequency of the primary keyword being searched alone are obtained bycounting search keywords. A first display unit 403 is used fordisplaying the acquired related keywords.

Preferably, the exemplary apparatus may further include a second displayunit, which is used for displaying related keywords having secondfeature values smaller than the threshold.

As the exemplary apparatus of displaying related keywords as shown inFIG. 4 can correspond to the exemplary method of displaying relatedkeywords of FIG. 3, description of the apparatus is relatively brief.Any missing details may refer to descriptions of related foregoingportions in the present invention.

As illustrated, the exemplary embodiments conduct a statistical analysison search keywords used by a user within a predetermined time period toensure timely relevance of the keywords. Using the second feature valuesas correlation parameters for determining displaying modes of therelated keywords, the exemplary embodiments provide to the user relatedkeywords that satisfy current usage trend, and thus results in a betteruser experience.

Second, the disclosed method and apparatus set up a keyword informationtable. During an update, only data in the table is updated accordingly,thus improving processing efficiency of the system.

Moreover, the step of counting search keywords and the step of computingfeature values may be concurrently executed using a multi-threadingmethod, thereby improving computing performance and efficiency of thesystem.

Furthermore, by storing first feature values in a cache, the disclosedmethod and apparatus further improve the computing performance andefficiency of the system. By recording the first feature values, thedisclosed method and apparatus may further provide the first featurevalues to the user for reference when displaying primary keywords.

Finally, from a service provider's perspective, the exemplaryembodiments of the present invention require no special secretalgorithms for implementation, are easy to operate and have a lowdevelopment cost.

The method and the apparatus of generating updating parameters, and themethod and the apparatus of displaying related keywords in the presentinvention have been described in details above. Exemplary embodimentsare employed to illustrate the concept and implementation of the presentinvention in this disclosure. The exemplary embodiments are only usedfor better understanding of the methods and the apparatuses of thepresent invention. Based on the concepts in this disclosure, one ofordinary skills in the art may modify the exemplary embodiments andtheir application. In general, contents in the present invention shouldnot be construed as limitations to the disclosed methods andapparatuses.

The invention claimed is:
 1. A method comprising: obtaining searchkeywords used by users within a predetermined time period; counting thesearch keywords to obtain a primary keyword, related keywords, co-searchfrequencies of the primary keyword and the related keywords beingsearched together, and a search frequency of the primary keyword beingsearched alone; computing correlation levels based at least in part onthe co-search frequencies of the primary keyword and the relatedkeywords being searched together; computing a first feature value basedat least in part on the search frequency of the primary keyword beingsearched alone; and computing second feature values based at least inpart on the first feature value, the correlation levels and theco-search frequencies of the primary keyword and the related keywords, asecond feature value serving as a parameter for determining whether acorresponding related keyword of the related keywords is to be displayedconstantly or in a rotating manner.
 2. The method as recited in claim 1,the method further comprising: recording the primary keyword, therelated keywords and the second feature values to form a keywordinformation table.
 3. The method as recited in claim 2, wherein thekeyword information table further contains the first feature valuecorresponding to the primary keyword.
 4. The method as recited in claim1, wherein obtaining the related keywords, computing the first featurevalues and computing the second feature values are concurrently executedusing a multi-threading method.
 5. The method as recited in claim 1,wherein prior to computing the first feature value, the method furthercomprises: filtering out one or more related keywords that satisfy afiltering rule.
 6. The method as recited in claim 1, wherein the primarykeyword includes a search keyword used by a user and/or a promulgatedkeyword posted by a promulgating user.
 7. An apparatus comprising: oneor more processors; memory; an acquisition unit stored in the memory andexecutable by the one or more processors, used for obtaining searchkeywords used by users within a predetermined time period; a statisticsunit stored in the memory and executable by the one or more processors,used for counting the search keywords to obtain a primary keyword,related keywords, co-search frequencies of the primary keyword and therelated keywords being searched together, and a search frequency of theprimary keyword being searched alone; a correlation computing sub-unitstored in the memory and executable by the one or more processors, usedfor computing correlation levels based at least in part on the co-searchfrequencies of the primary keyword and the related keywords beingsearched together; a first computation unit stored in the memory andexecutable by the one or more processors, used for computing a firstfeature value based at least in part on the search frequency of theprimary keyword being searched alone; and a second computation unitstored in the memory and executable by the one or more processors, usedfor computing second feature values based at least in part on the firstfeature value, the correlation levels and the co-search frequencies ofthe primary keyword and the related keywords being searched together,wherein a second feature value serves as a parameter for determiningwhether a corresponding related keyword of the related keywords is to bedisplayed constantly or in a rotating manner.
 8. The apparatus asrecited in claim 7, the apparatus further comprising: a recording unit,used for recording the primary keyword, the related keywords and thesecond feature values to form a keyword information table.
 9. Theapparatus as recited in claim 8, the apparatus further comprising: anaddition unit connected with the recording unit and used for recordingthe first feature value in the keyword information table.
 10. Theapparatus as recited in claim 7, the apparatus further comprising: afiltering unit connected with the statistics unit and used for filteringout one or more search keywords that satisfy a filtering rule.